{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Multiclass Classification of White Wine Quality with Over-sampling and k-NN\n",
    "\n",
    "## Wine Data\n",
    "Data from http://archive.ics.uci.edu/ml/datasets/Wine+Quality\n",
    "\n",
    "### Citations\n",
    "P. Cortez, A. Cerdeira, F. Almeida, T. Matos and J. Reis. \n",
    "Modeling wine preferences by data mining from physicochemical properties.\n",
    "In Decision Support Systems, Elsevier, 47(4):547-553. ISSN: 0167-9236.\n",
    "\n",
    "Available at:\n",
    "- [@Elsevier](http://dx.doi.org/10.1016/j.dss.2009.05.016)\n",
    "- [Pre-press (pdf)](http://www3.dsi.uminho.pt/pcortez/winequality09.pdf)\n",
    "- [bib](http://www3.dsi.uminho.pt/pcortez/dss09.bib)\n",
    "\n",
    "Dua, D. and Karra Taniskidou, E. (2017). UCI Machine Learning Repository [http://archive.ics.uci.edu/ml]. Irvine, CA: University of California, School of Information and Computer Science.\n",
    "## Setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "\n",
    "white_wine = pd.read_csv('../../ch_10/data/winequality-white.csv', sep=';')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## EDA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>fixed acidity</th>\n",
       "      <th>volatile acidity</th>\n",
       "      <th>citric acid</th>\n",
       "      <th>residual sugar</th>\n",
       "      <th>chlorides</th>\n",
       "      <th>free sulfur dioxide</th>\n",
       "      <th>total sulfur dioxide</th>\n",
       "      <th>density</th>\n",
       "      <th>pH</th>\n",
       "      <th>sulphates</th>\n",
       "      <th>alcohol</th>\n",
       "      <th>quality</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7.0</td>\n",
       "      <td>0.27</td>\n",
       "      <td>0.36</td>\n",
       "      <td>20.7</td>\n",
       "      <td>0.045</td>\n",
       "      <td>45.0</td>\n",
       "      <td>170.0</td>\n",
       "      <td>1.0010</td>\n",
       "      <td>3.00</td>\n",
       "      <td>0.45</td>\n",
       "      <td>8.8</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6.3</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.34</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.049</td>\n",
       "      <td>14.0</td>\n",
       "      <td>132.0</td>\n",
       "      <td>0.9940</td>\n",
       "      <td>3.30</td>\n",
       "      <td>0.49</td>\n",
       "      <td>9.5</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8.1</td>\n",
       "      <td>0.28</td>\n",
       "      <td>0.40</td>\n",
       "      <td>6.9</td>\n",
       "      <td>0.050</td>\n",
       "      <td>30.0</td>\n",
       "      <td>97.0</td>\n",
       "      <td>0.9951</td>\n",
       "      <td>3.26</td>\n",
       "      <td>0.44</td>\n",
       "      <td>10.1</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7.2</td>\n",
       "      <td>0.23</td>\n",
       "      <td>0.32</td>\n",
       "      <td>8.5</td>\n",
       "      <td>0.058</td>\n",
       "      <td>47.0</td>\n",
       "      <td>186.0</td>\n",
       "      <td>0.9956</td>\n",
       "      <td>3.19</td>\n",
       "      <td>0.40</td>\n",
       "      <td>9.9</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7.2</td>\n",
       "      <td>0.23</td>\n",
       "      <td>0.32</td>\n",
       "      <td>8.5</td>\n",
       "      <td>0.058</td>\n",
       "      <td>47.0</td>\n",
       "      <td>186.0</td>\n",
       "      <td>0.9956</td>\n",
       "      <td>3.19</td>\n",
       "      <td>0.40</td>\n",
       "      <td>9.9</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   fixed acidity  volatile acidity  citric acid  residual sugar  chlorides  \\\n",
       "0            7.0              0.27         0.36            20.7      0.045   \n",
       "1            6.3              0.30         0.34             1.6      0.049   \n",
       "2            8.1              0.28         0.40             6.9      0.050   \n",
       "3            7.2              0.23         0.32             8.5      0.058   \n",
       "4            7.2              0.23         0.32             8.5      0.058   \n",
       "\n",
       "   free sulfur dioxide  total sulfur dioxide  density    pH  sulphates  \\\n",
       "0                 45.0                 170.0   1.0010  3.00       0.45   \n",
       "1                 14.0                 132.0   0.9940  3.30       0.49   \n",
       "2                 30.0                  97.0   0.9951  3.26       0.44   \n",
       "3                 47.0                 186.0   0.9956  3.19       0.40   \n",
       "4                 47.0                 186.0   0.9956  3.19       0.40   \n",
       "\n",
       "   alcohol  quality  \n",
       "0      8.8        6  \n",
       "1      9.5        6  \n",
       "2     10.1        6  \n",
       "3      9.9        6  \n",
       "4      9.9        6  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "white_wine.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>fixed acidity</th>\n",
       "      <th>volatile acidity</th>\n",
       "      <th>citric acid</th>\n",
       "      <th>residual sugar</th>\n",
       "      <th>chlorides</th>\n",
       "      <th>free sulfur dioxide</th>\n",
       "      <th>total sulfur dioxide</th>\n",
       "      <th>density</th>\n",
       "      <th>pH</th>\n",
       "      <th>sulphates</th>\n",
       "      <th>alcohol</th>\n",
       "      <th>quality</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>4898.000000</td>\n",
       "      <td>4898.000000</td>\n",
       "      <td>4898.000000</td>\n",
       "      <td>4898.000000</td>\n",
       "      <td>4898.000000</td>\n",
       "      <td>4898.000000</td>\n",
       "      <td>4898.000000</td>\n",
       "      <td>4898.000000</td>\n",
       "      <td>4898.000000</td>\n",
       "      <td>4898.000000</td>\n",
       "      <td>4898.000000</td>\n",
       "      <td>4898.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>6.854788</td>\n",
       "      <td>0.278241</td>\n",
       "      <td>0.334192</td>\n",
       "      <td>6.391415</td>\n",
       "      <td>0.045772</td>\n",
       "      <td>35.308085</td>\n",
       "      <td>138.360657</td>\n",
       "      <td>0.994027</td>\n",
       "      <td>3.188267</td>\n",
       "      <td>0.489847</td>\n",
       "      <td>10.514267</td>\n",
       "      <td>5.877909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.843868</td>\n",
       "      <td>0.100795</td>\n",
       "      <td>0.121020</td>\n",
       "      <td>5.072058</td>\n",
       "      <td>0.021848</td>\n",
       "      <td>17.007137</td>\n",
       "      <td>42.498065</td>\n",
       "      <td>0.002991</td>\n",
       "      <td>0.151001</td>\n",
       "      <td>0.114126</td>\n",
       "      <td>1.230621</td>\n",
       "      <td>0.885639</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>3.800000</td>\n",
       "      <td>0.080000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>0.009000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>0.987110</td>\n",
       "      <td>2.720000</td>\n",
       "      <td>0.220000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>6.300000</td>\n",
       "      <td>0.210000</td>\n",
       "      <td>0.270000</td>\n",
       "      <td>1.700000</td>\n",
       "      <td>0.036000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>108.000000</td>\n",
       "      <td>0.991723</td>\n",
       "      <td>3.090000</td>\n",
       "      <td>0.410000</td>\n",
       "      <td>9.500000</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>6.800000</td>\n",
       "      <td>0.260000</td>\n",
       "      <td>0.320000</td>\n",
       "      <td>5.200000</td>\n",
       "      <td>0.043000</td>\n",
       "      <td>34.000000</td>\n",
       "      <td>134.000000</td>\n",
       "      <td>0.993740</td>\n",
       "      <td>3.180000</td>\n",
       "      <td>0.470000</td>\n",
       "      <td>10.400000</td>\n",
       "      <td>6.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>7.300000</td>\n",
       "      <td>0.320000</td>\n",
       "      <td>0.390000</td>\n",
       "      <td>9.900000</td>\n",
       "      <td>0.050000</td>\n",
       "      <td>46.000000</td>\n",
       "      <td>167.000000</td>\n",
       "      <td>0.996100</td>\n",
       "      <td>3.280000</td>\n",
       "      <td>0.550000</td>\n",
       "      <td>11.400000</td>\n",
       "      <td>6.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>14.200000</td>\n",
       "      <td>1.100000</td>\n",
       "      <td>1.660000</td>\n",
       "      <td>65.800000</td>\n",
       "      <td>0.346000</td>\n",
       "      <td>289.000000</td>\n",
       "      <td>440.000000</td>\n",
       "      <td>1.038980</td>\n",
       "      <td>3.820000</td>\n",
       "      <td>1.080000</td>\n",
       "      <td>14.200000</td>\n",
       "      <td>9.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       fixed acidity  volatile acidity  citric acid  residual sugar  \\\n",
       "count    4898.000000       4898.000000  4898.000000     4898.000000   \n",
       "mean        6.854788          0.278241     0.334192        6.391415   \n",
       "std         0.843868          0.100795     0.121020        5.072058   \n",
       "min         3.800000          0.080000     0.000000        0.600000   \n",
       "25%         6.300000          0.210000     0.270000        1.700000   \n",
       "50%         6.800000          0.260000     0.320000        5.200000   \n",
       "75%         7.300000          0.320000     0.390000        9.900000   \n",
       "max        14.200000          1.100000     1.660000       65.800000   \n",
       "\n",
       "         chlorides  free sulfur dioxide  total sulfur dioxide      density  \\\n",
       "count  4898.000000          4898.000000           4898.000000  4898.000000   \n",
       "mean      0.045772            35.308085            138.360657     0.994027   \n",
       "std       0.021848            17.007137             42.498065     0.002991   \n",
       "min       0.009000             2.000000              9.000000     0.987110   \n",
       "25%       0.036000            23.000000            108.000000     0.991723   \n",
       "50%       0.043000            34.000000            134.000000     0.993740   \n",
       "75%       0.050000            46.000000            167.000000     0.996100   \n",
       "max       0.346000           289.000000            440.000000     1.038980   \n",
       "\n",
       "                pH    sulphates      alcohol      quality  \n",
       "count  4898.000000  4898.000000  4898.000000  4898.000000  \n",
       "mean      3.188267     0.489847    10.514267     5.877909  \n",
       "std       0.151001     0.114126     1.230621     0.885639  \n",
       "min       2.720000     0.220000     8.000000     3.000000  \n",
       "25%       3.090000     0.410000     9.500000     5.000000  \n",
       "50%       3.180000     0.470000    10.400000     6.000000  \n",
       "75%       3.280000     0.550000    11.400000     6.000000  \n",
       "max       3.820000     1.080000    14.200000     9.000000  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "white_wine.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 4898 entries, 0 to 4897\n",
      "Data columns (total 12 columns):\n",
      "fixed acidity           4898 non-null float64\n",
      "volatile acidity        4898 non-null float64\n",
      "citric acid             4898 non-null float64\n",
      "residual sugar          4898 non-null float64\n",
      "chlorides               4898 non-null float64\n",
      "free sulfur dioxide     4898 non-null float64\n",
      "total sulfur dioxide    4898 non-null float64\n",
      "density                 4898 non-null float64\n",
      "pH                      4898 non-null float64\n",
      "sulphates               4898 non-null float64\n",
      "alcohol                 4898 non-null float64\n",
      "quality                 4898 non-null int64\n",
      "dtypes: float64(11), int64(1)\n",
      "memory usage: 459.2 KB\n"
     ]
    }
   ],
   "source": [
    "white_wine.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'quality score')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = white_wine.quality.value_counts().sort_index(\n",
    "    ascending=False\n",
    ").plot.barh(title='White Wine Quality Scores', figsize=(12, 3))\n",
    "for bar in ax.patches:\n",
    "    ax.text(\n",
    "        bar.get_width(), \n",
    "        bar.get_y() + bar.get_height()/4, \n",
    "        f'{bar.get_width()/white_wine.shape[0]:.1%}'\n",
    "    )\n",
    "plt.xlabel('count of wines')\n",
    "plt.ylabel('quality score')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## White wine quality multi-class classification"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Train test split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "y = white_wine.quality\n",
    "X = white_wine.drop(columns=['quality'])\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(\n",
    "    X, y, test_size=0.15, random_state=0, stratify=y\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Over-sampling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "from imblearn.over_sampling import RandomOverSampler\n",
    "\n",
    "X_train_oversampled, y_train_oversampled = RandomOverSampler(random_state=0).fit_resample(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7    1868\n",
       "6    1868\n",
       "5    1868\n",
       "4    1868\n",
       "3    1868\n",
       "9    1868\n",
       "8    1868\n",
       "dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.Series(y_train_oversampled).value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Building a model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "pipeline = Pipeline([\n",
    "    ('scale', StandardScaler()), \n",
    "    ('knn', KNeighborsClassifier())\n",
    "])\n",
    "\n",
    "search_space = {\n",
    "    'knn__n_neighbors' : np.arange(1, 10)\n",
    "}\n",
    "\n",
    "grid = GridSearchCV(pipeline, search_space, scoring='f1_macro', cv=5).fit(X_train_oversampled, y_train_oversampled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'knn__n_neighbors': 1}"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid.best_params_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Evaluating the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "preds = grid.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           3       0.00      0.00      0.00         3\n",
      "           4       0.45      0.42      0.43        24\n",
      "           5       0.68      0.68      0.68       219\n",
      "           6       0.66      0.71      0.68       330\n",
      "           7       0.53      0.45      0.49       132\n",
      "           8       0.39      0.46      0.42        26\n",
      "           9       0.00      0.00      0.00         1\n",
      "\n",
      "   micro avg       0.63      0.63      0.63       735\n",
      "   macro avg       0.39      0.39      0.39       735\n",
      "weighted avg       0.62      0.63      0.63       735\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\molinstefanie\\packt\\venv\\lib\\site-packages\\sklearn\\metrics\\classification.py:1143: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "c:\\users\\molinstefanie\\packt\\venv\\lib\\site-packages\\sklearn\\metrics\\classification.py:1143: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "c:\\users\\molinstefanie\\packt\\venv\\lib\\site-packages\\sklearn\\metrics\\classification.py:1143: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report\n",
    "print(classification_report(y_test, preds))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x135e4470>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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IhteBnsAiETkaqAr8CMwFZonI33E6GdoBKwKdKJgaXCfgOI3E6jTGmJjn5Y2+IvIC0B1oKCLZwERgOjDdvXXkEDDCzT+fi8hs4AugALguUA8qBJfg1gONgW0VfhfGmIThZRNVVS8p46nhZRw/CZgU7PmDSXANgS9EZAVw0C/QgGCDhNPEO8ayePEiUlPTePX1tyIef9Zzz/D6a68gIrRtdzQT7rmfatWqeXb+W89tw5mtU8ndn8/lMz/9zXMXndyUUV1bMXDyCnYfKKBW1WTG92lHep1qJCcJL63+gQVf7CjjzKH78x8HUr1mTZKSkkhOTubex2fw8rOTWfPfxUiSULd+Ktf8ZQIN0hp5FrOkTZs2Mu7WMcWPt2Zv4ZprRzNs+IiwxfS3dMliHnxgEoW+QgYPuZArrw6mUzD+Y/tLtKFad1X05O6q9ntxrt0VqGqnip6rLAMGXcDFw4Zzx7jbvD51uXbk5PDSrJm8NOctqlevzthbbuLdBfM5f+Bgz2Is+GIncz7dzrje7X6zv1HtqpzSsh7b9xT/zmFQh8Zs2pXHuLlfUa9GCs+NOIn/fLWTgkLvri6Mf/Bf1PFb1Dpr6HAuHPEnAN55/SXmPD+VK24Y61m8klq1as2s2XMA8Pl89Du3Oz16nhO2eP58Ph/3T7qHfz/1NBkZGQy7aCjde/SkTdu2CR27pDjKb+XfB6eqHwJfAXXc7Ut3X7B6qGrHcCQ3gFM6nUrdevXCceqgFPh8HDx4gIKCAg7k5dGoUbqn51+7dQ97DxYctv/6bq3595Lv8e8lV6BmlWQAalRJZu+BAnweJrfS1KxVu/jngwfyIvrbfeXyZTTLzKRJ04C3Qnlm/bq1ZGa2pHlmJlWqVqVPvywWLXw/4WOXlJQkQW/RFkwv6u+Bv+Is3CzA4yJyi6q+Euayxbz0jAyGj7ic83v3olr1apx+ZhfO6Nwl7HE7H9WAnfsO8u2P+3+zf86n27h/wO949epO1KySzN3zvwl8k1CIROCBcaMREXr2G0zPfk5NdfYzT/LRf+ZTs1Ztxj34Lw8jBvbOgvn07pMVsXg7cnJo3KRx8eP0jAzWrV2b8LFLiqcmajAjGcYDp6rqCFX9I87QiDuDPL8C74rI6rLuYPa/03na1FDvB4yuPXt2s3jhB7wx/z3efu9DDuTlMf+tuWGNWS0lieGnNefp/2457LnTWjZgw85fGPLUKq56/jNu7NGamlWTPYs94e9TmfTEc9xy36P8582X+WrdGgB+f9m1PDbzLTr36MN7b77sWbxA8vMPsfjDDzjnvHLHW3tGS/l1EakvezRjlxbXq6Fa4RZMgkvyGwsG8FOQrwPooqonA32B60Ska8kDVHWKqnZS1U5XXhWdi6YVtWLZf2narBkNUlNJqVKFHr3OYe1nn4Q1ZtN61WlStzrThnfgxStOplHtakwZ1oHUmlXoc3w6izf8BMDW3QfYtucgLRrU8Cx2UedBvfqpnNK5O99+/cVvnu/cozcrP/rAs3iBLP1oCcceexxpaQ0jEg8gI6Mx27dtL368IyeH9HRvL0nEYuySQhmqFW3BJKoFIvKOiFwmIpcB84D5wZxcVX9w/94BzKGcgbHxpnHjJqxb+xkH8vJQVVYuX0br1m3CGnPjT/sZPGUlF09fw8XT17Bz30FGzvqMXfvz2bH3IKe0cDoAGtSsQmaD6mzbfcCTuAcO5JG3/5fin9evWU7zVm3YvnVz8TFrli2mSWYrT+KV552359G7b+SapwDHtz+BzZs3kZ29hfxDh1gwfx7devRM+NglxVMNrtxrcKp6i4gMAbrgXIOboqpzynudiNTCqf3tdX8+D7jnSAtc0u23jGHVyhXk5v7Meb26Mura0QwecqHXYUrV/sQO9Dq3N8MvHkJycjLHHPs7Bg/9vacx7uzbjo7N61GvegovX3kKTy/bwvzPS7/1Y8byLdx+XjumD++AIEz56Ht2Hzi8g6Ii9vy8i0fvuQVwevQ69+hNh05n8o97b2Nb9veIJNEwozGXj77dk3iBHMjLY8Wyjxl/591hj+UvJSWFseMnMGrkVRQW+hg0eAht27Yr/4VxHrukGMhbQZNwDVBwB8cWJcIUYJZ7k16Z8vI9vSYetHxfYTTCAjDA1mSIuCoplXcSneoppQ5YD0mvx/8b9Pf0/dFnRjUdllmDE5GPVPUsEdnLb0fsC6CqWjfQiVX1O6CDN8U0xsSKaC7SFKoyE5yqnuX+Hb1ftcaYmBNH+a38TgZ3NpFy9xljKoeE6mTAmf+8mIikAKeEpzjGmFgXAwMUghboGtxYYBxQQ0T2FO3Gmb4kvu7INcZ4JhaGYAWrzCaqqv6fe/3tr6pa193qqGqaqoZvNLUxJqZJCH+iLZj+8hUiUjyaXUTqi8igMJbJGBPDkiT4LdqCSXATVXV30QNVzcWZddMYUwklWidDaUnQVuMyppKKgbwVtGAS1Sp3kYcncG74HQ2sDmupjDExKyFu9PUzGmd6pJdwelHfBa4LZ6EiLZr/YQ8NiN5wqa4XjI9a7I/m3B+12Ec3rl3+QWGSCMPE4qkXNZjB9r8A4R9BbYyJC3FUgQt4H9yjqvpnEXmTUlaPjpVFZ4wxkZUoTdSi4VgPR6Igxpj4ED/pLfBg+9Xu36EsMGOMSXCxcPtHsAI1UddRStO0iKqeGJYSGWNiWhz1MQRsovZ3/y7qMS1qsl4K7D/8cGNMZZAQvaiq+j2AiHRRVf+18G4XkaWEYfpxY0zsi6cmajA35dQSkbOKHohIZ6BW+IpkjIll8TQWNZgbfa8EprsD7hXYDVwR1lIZY2JWQtXgVHW1qnYATgQ6qmpHVV0T/qIZY2KRhLCVey6R6SKyQ0TW++37q4h8JSJrRWSOiNT3e26siGwQka9FpNxVv4OZsjxDRKYBL6nqbhE5TkSuDKLsxpgElJwkQW9BeAboU2Lfe0B7906Nb4CxACJyHHAxzizjfYAnRSQ50MmDaaI+AzwNFA1c/AZnXOq0YEofbhPvGMvixYtITU3j1dffinj8/n16UrNmLZKTk0lOTmbmi6+GNd5NIwZSvWZNkpKSSE5O5p7HZvDazCksWvAGdeo5v+guHHEtHU/rUs6Zytc8oz5T7/0jGWl1KVRl+qtLeeKFRUy4Nov+3U6kUJWdu/YycuJMtu3cTf/uJzBhVH8KVSnwFXLrX1/h40+/O+JyANzwxwHUqOG876TkFCb9cwbLFv+HV5+bwg9bNnHvY89w1NHHeRKrLJs2bWTcrWOKH2/N3sI1145m2PARYY1bZOmSxTz4wCQKfYUMHnIhV149MiJxS/Kyiaqqi0WkVYl97/o9XAYMdX8eCLyoqgeBjSKyAWcx+f+Wdf5gElxDVZ3tTmGOqhaIiC+YwrtVy6lAe5zrd1eoapmFqYgBgy7g4mHDuWPcbV6eNiT/njaDBg0aRCzeuAf+VZzMivQedAlZQ4d7GqfAV8jtf3+NT7/KpnbNanw86zbeX/4Vjzz7Pvc8OQ+Aay/pxtiRfblh0ossXP41by1aB0D7dk2Z+eAVdLzgPs/KM/6hydT1e9+Zrdpw04SHmPbY/3kWI5BWrVoza7az1K/P56Pfud3p0fOciMT2+XzcP+ke/v3U02RkZDDsoqF079GTNm3bRiS+v1Dym4iMBPwz8RRVDWXJgytwKlQAzXASXpFsd1+Zgklwv4hIGu5NvyJyBk5HQzD+ASxQ1aEiUhWoGeTrgnZKp1PZujXb69MaYPuPe9j+o7Mcx779B/lq43aaNqrPV99tLz6mZo1qFC0e/kveoeL9tWpUI0xrihdr1qJ1eAMEsHL5MpplZtKkacDvl2fWr1tLZmZLmmdmAtCnXxaLFr4flQQXylhUN5lVaA0XERkPFADPF+0qLUSgcwST4MYAc4E27v1vjfi1yhiocHWBrsBlAKp6CGfBmoQiCNddcyUiMOTCi7hg6EXhDsiD40cjIvToO5ie/QYD8J83X2bp+/Np3e53DLv6RmrVCbgud8haNEml4zHNWbl+EwB3XXc+l/Y/jd378ugz8rHi4wb0OJF7Rg+gUWodLrhhsmfxBeGBcdcDQq+swfTqd4Fn566IdxbMp3efrIjF25GTQ+MmjYsfp2dksG7t2ojF9xeJTlQRGYEz2KCXavGvymwg0++w5sAPgc4TMMGJSBJQHegGHIOTQb9W1fwgyngUsBN4WkQ64EySeaM7/ZJ/jOIq7ONP/psrr4rOdYWKmj5jFo3SM9j1009ce80VtGp1FCd3OjVs8Sb8bSoN0hqxO3cXD467nqaZLemVNYRBl1wJIrw6YzKznvoHV4+507OYtWpU5YWHr+KWh19l7y8HALjriTe564k3ufmK8/jTRV25b/J8AOYuXMvchWvpcnIbJlybRdaf/ulJGe565Nf3/X+3X0/TzFb87oSTPTl3qPLzD7H4ww+4/sabIhZTS6moROt2jXDHFZE+wG1AN1X1HzU1F5jlTsDbFGgHrAh0roC9qKpaCPxNVQtU9XNVXR9kcgMneZ4M/EtVTwJKnVdOVaeoaidV7RRvyQ2gUXoGAKlpafToeQ7r14f3t2qDtEYA1KufSqfO3fn26y+o1yCNpORkkpKS6N53EN9+87ln8VJSknjh4at56e1VvPHBZ4c9P/vtlQzq1fGw/UvXfMtRzRuSVt+be8J/8767dOfbr7x7j6Fa+tESjj32ONLSGkYsZkZGY7Zv+/XSwI6cHNLT0yMW31+ySNBbeUTkBZxOgmNEJNu9Q+OfQB3gPRH5VEQmA6jq58Bs4AtgAXCdqgbsDwhmJMO7IjJEQk/b2UC2qi53H7+Ck/ASRt7+/fzyy77in5f9dylt2x4dtngHDuSRt/+X4p/XrVlOZqs25O76sfiYVR8vonnLNp7FnDzxUr7euJ3HZn5QvK9Ni0bFP2d1O5FvNuUAcFTmr1/4jsc2p2qVFH7K/U2FvUIOe9+rl5HZyrv3GKp33p5H776Ra54CHN/+BDZv3kR29hbyDx1iwfx5dOvRM6JlKOLlSAZVvURVm6hqFVVtrqrTVLWtqma699x2VNU/+R0/SVXbqOoxqvp2eecP9hpcLaBARA7gNFNVVQNe5FHV7SKyRUSOUdWvgV44mddTt98yhlUrV5Cb+zPn9erKqGtHM3jIhV6HKdVPu37i5j9fDzi9XH369qfzWWeHLd6en3fx6L23AFDo83Fm996c2OlMJv91It9/9w2C0DCjCVfc4M2ytZ07HsWl/U9n3TdbWfaiU/me+M+5XDaoM+1aplNYqGzetosbJr0IwOBeHRnW/3TyC3wcOJjPH26b7kk5dv/8E4/cfSsAPl8BXXr0ocOpnVm5dCHPPvkwe3b/zEN33kTLNkcz9v7HPYlZlgN5eaxY9jHj77w7rHFKSklJYez4CYwaeRWFhT4GDR5C27btIlqGIrEwBCtYomHs6hKRjji3iVQFvgMuV9Wfyzo+Lz9wj0i4+AqjEhaAL7buiVrsbkNsTYZIi/aaDNVTjny+yr+8+XXQX5i/nX9MVNNhoPng0oFxQFtgLfCAqob0bVTVT4FOR1RCY0xMiacaXKBfJzNwOgYex7ng91iAY40xlYRI8Fu0BboG11hVi9ow74iIDbA3xpASC5krSIESnIhIA369ezjZ/7Gq7gp34YwxsSeO8lvABFcP5+Zc/7dTVItTnBt5jTGVTEIsG6iqrSJYDmNMnIij/BbUfXDGGFMsnnpRLcEZY0IS5ESWMcESnDEmJHGU3wLe6Jsa6IXWi2pM5SRHPhgiYgLV4Fbj9JaWNcmc572o0bp4Gc0q97FN60Qt9mdvPxS12Dv3Hoxa7GgK9ySgkZAQNThVjd50qcaYmBVPCS6YVbVERIaLyJ3u4xYiclr4i2aMiUUiEvQWbcFMbfAkcCYwzH28F3gibCUyxsS05KTgt2gLphf1dFU9WUQ+AVDVn90FZIwxlVBCjGTwk+8urlq0qlYjoDCspTLGxKyEugaHM03SHCBdRCYBHwHRm63QGBNViTJdEgCq+ryIrMaZclyAQar6ZdhLZoyJSUmJcB9ciRt9dwAv+D9nN/oaUznFQs0sWMHe6NsC+Nn9uT6wGbD75IyphFLi6CJcuTf6umsSzlXV+e7jvsA5kSmeMSbWxFMNLph82QVbAAAVCUlEQVROhlOLkhuAuxZht/AVyRgTy5JEgt6iLZjbRH4UkTuAmThN1uHAT2EtVYiWLlnMgw9MotBXyOAhF3Ll1SMjEnfiHWNZvHgRqalpvPr6WxGJ6W/Wc8/w+muvICK0bXc0E+65n2rVqoUl1qGDB7n9hivJzz+Ez+ejS7dzuPSKUagqz019gqWL3iMpKZm+A4cyYOiw8k8Yov379vL0Y/ezdfN3CHD5jXfQuHkLJj94Bz/mbKNhRhNG3T6JWrUDLtd7RDZt2si4W8cUP96avYVrrh3NsOEjwhazSLQ/a/5iIG8Frdx1Ud3OholAV3fXYuDu8joZROQY4CW/XUcBE1T10bJec6Ag9HVRfT4fA7J68++nniYjI4NhFw3lgb/+nTZt2wZ9jooOgF69aiU1a9bkjnG3VfhDl++r2C2FO3JyuPqyS3lpzltUr16dsbfcROezunL+wMFBn2N77oGgj1VVDuTlUaNmTQoK8rnt+iu4evQtZH+/kbWfrOTPY+8hKSmJ3J93Ub9BwIlogNAH20/9+z0cfXwHuvYeSEF+PocOHuCt2c9Qq049si78I/NensH+fXu48PLryz3XiZn1QopdGp/PR79zu/PMzBdp0rRZ0K9LqeDt/V581gBqVDnyLtBnVm4O+htz2aktopoOy/3XVtVdqnojTrP0bFW9MZgeVFX9WlU7qmpH4BRgP879dJ5av24tmZktaZ6ZSZWqVenTL4tFC9/3OkypTul0KnXrHfmXpaIKfD4OHjxAQUEBB/LyaNQoPWyxRIQaNWs6cQsKKCgoQESY/8bLXDxiJElJzkcpmOQWqrz9v/DN559w9nkDAEipUoWatevwyfIldOnVD4AuvfqxZtliz2OXZeXyZTTLzAwpuR2JaH/W/CVUE1VETsBZIzXVffwjMEJV14cQpxfwrap+X6FSBrAjJ4fGTRoXP07PyGDd2rVeh4k56RkZDB9xOef37kW16tU4/cwunNG5S1hj+nw+bho5jG1bt5A16CKOOe4Etv+QzZKF77JsyQfUrdeAa268labNW3oad+f2rdSp24Dpj97Llo0baNn2GIaNHMOe3F3UT20IQP3UhuzN/dnTuIG8s2A+vftkRSxeLImFxBWsYOrL/wbGqGpLVW0J/AWYEmKci/G7j86fiIwUkVUismraU6GeFrSUVm0szGIQbnv27Gbxwg94Y/57vP3ehxzIy2P+W3PDGjM5OZnHpr3E0y+/wzdfruf77zaQn3+IqlWr8siUWfQ+/wL+8cDdnsf1+Xx8/+3XdO93AXc9NoNq1Wow7+UZnscJVn7+IRZ/+AHnnNc7amWIJglhK/dcIjeJyOcisl5EXhCR6iLSWkSWi8j/ROSlIxn7HkyCq6WqC4seqOoioFawAdzCDQBeLu15VZ2iqp1UtVNFOgcyMhqzfdv24sc7cnJITw9fUy1WrFj2X5o2a0aD1FRSqlShR69zWPvZJxGJXbtOHU44qROrV3xMWqMMOnd17ho68+yebPruf57HS22YToOGjWhzTHsAOnXpyeZvv6Zu/VRyd/0IQO6uH6lTv4HnsUuz9KMlHHvscaSlNYxIvFjj1VAtEWkG3AB0UtX2QDJOZehB4BFVbYdz/+2VFS1rMAnuOxG5U0RaudsdwMYQYvQF1qhqTsWKGNjx7U9g8+ZNZGdvIf/QIRbMn0e3Hj3DESqmNG7chHVrP+NAXh6qysrly2jduk3Y4u3O3cW+vXsBOHjwAJ+uWk7zFq0446zurF2zAoD1n66mafMWnseu1yCN1IYZbMt2rnB88dlKmrZozUmnn83S9507mJa+P5+TTj/b89ileeftefTuWzmbp+D5fHApQA0RSQFqAtuAnsAr7vPPAoMqWtZgbhO5ArgbeA2n1rkYuDyEGJdQRvPUCykpKYwdP4FRI6+isNDHoMFDaNu2XbjC/cbtt4xh1coV5Ob+zHm9ujLq2tEMHnJhRGK3P7EDvc7tzfCLh5CcnMwxx/6OwUN/H7Z4u376kUfvn0BhYSGFWshZ3c/ltM5dOe6Ek/jbfeN44+XnqV6jBjfcOiEs8S/901+Y8vBEfAX5NGrcjCv+fAdaqPzrgfEseXcuaY0aM2rspLDE9ncgL48Vyz5m/J3eN8UDieZnraRQ+oFFZCTg3zSboqpTAFR1q4g8jDMyKg94F2cEVa6qFrjHZwMV7skp9zaRIyEiNYEtwFGquru84ytym4gXojlPfkVvE/FCKLeJeC2aazJ4cZtIRVX0NhGveHGbyMuf/hD0N+bCjk3LjCciDYBXgYuAXJzLWK8CE1W1rXtMJjBfVU+oSFkDDbYPeMVaVQeUd3JV3Q+kVaBcxpgY5WEn3jnARlXd6Z73NaAzUF9EUtxaXHPgh4oGCNREPROn9vUCsJzgOkWMMQnOwzroZuAMt6WXh3M72SpgITAUeBEYAbxR0QCBElxj4Fyca2jDgHnAC6r6eUWDGWPin1c1OFVdLiKvAGuAAuATnFvQ5gEvish97r5pFY0RaDYRH7AAWCAi1XAS3SIRuUdVH69oQGNMfPOyKaeqE3GGgvr7DvBk5b6AvahuYsvCSW6tcKYvf82LwMaY+JQcRzfSB+pkeBZoD7yNM7g+lKFZxpgEFUf5LWAN7g/AL8DRwA1+7W4BVFXDNy+NMSZmSRz1Nwa6BhcDy7YaY2JNotTgjDHmMAmxqpYxxpTGanBxJpr/YVWiOHSnSYPqUYvdsE54plYPRr4vemPzqiTANy6e5oNLgH9uY0wkxdGqgZbgjDGhSYheVGOMKU0ctVAtwRljQmM1OGNMwrJrcMaYhGW9qMaYhBU/6c0SnDEmRFaDM8YkrPhJb5bgjDGhiqMMZwnOGBOSeGqiJsSUSEuXLGZAVm/69zmXaU9NqRSxJ94xlh5dz2TIoP4Ri+lv75493DrmRoYM6MfQgVms/eyTsMW6767x9Ot1Fpde+OtCbo8/8lcuuiCL4b8fxG1/Gc3evXsSLnZJ0fyc+5MQtmgLa4ITkZtE5HMRWS8iL4iI56O7fT4f90+6hycnT2XO3HksmP8W327Y4HWYmIs9YNAFPDl5akRilebhB++nc5ezeHXufF54ZQ6tW7cJW6ys8wfzyD9/+4U+7YzOPD/7DWbOfp0WLVoxY/pTCRfbXzQ/a4eJowwXtgQnIs2AG4BOqtoeSAYu9jrO+nVrycxsSfPMTKpUrUqfflksWvi+12FiLvYpnU6lbr3oLGC8b98+Plm9ioEXDAWgSpWq1KkbvgmeTzql02Hv9fQzu5CS4lxhOf6EDuzYsT3hYvuL5metJAnhT7SFu4maAtQQkRSgJkewgGtZduTk0LhJ4+LH6RkZ5OTkeB0m5mJH09bsLdRPTeXuO8cx7PcXcO/EO8jbvz9q5Xnrjdc4s/PZCR07lj5rIsFv0Ra2BKeqW4GHcRZ33QbsVtV3Sx4nIiNFZJWIrKrIdQXl8Lm9PFx5O2ZjR5PP5+PrL79g6O8vZtbs16hRoybPRKCZVppnpk4mOSWZ3v3OT+jYsfRZi6MWavh6UUWkATAQaA3kAi+LyHBVnel/nKpOwVnslQMFpfwvliMjozHbt/3aRNiRk0N6evqRFD0uYkdTekYG6RkZtD+xAwC9zj0vKglu3puvs3TJhzw+eXrEv+yRjh1Ln7V4+iUezibqOcBGVd2pqvk466l29jrI8e1PYPPmTWRnbyH/0CEWzJ9Htx49vQ4Tc7GjqWHDRmRkNGHTxo0ArFi+jKOOahvRMvx36RJmPjOVhx59guo1aiR87Fj6rMVTE1VUwzN9s4icDkwHTgXygGeAVar6eFmvqUgNDmDJ4g956IH7KSz0MWjwEK6+ZlRFTlMhRxq7ov/8t98yhlUrV5Cb+zOpaWmMunY0g4dcGNI5CgoLKxYc+PqrL7nvrjvJz8+nWfNMJt47ibp1g+/0yC8I/o1PGHsza1avIDc3l9TUNK760/XMmD6F/Px86rkdAMef0IHbxt8V6tuIeOya1ZIrXBYvPufVU4685fjZ5r1B/+d1aFEnqmkubAkOQETuBi4CCoBPgKtU9WBZx1c0wcWzMP7zl+tIEtyRCiXBJZIjSXBe8CTBbQkhwWUmcIILlSW4yLIEF3mJkODWbtkX9H/eiZm1o5rgEmIkgzEmcry+BiciySLyiYi85T5uLSLLReR/IvKSiFStaFktwRljQhKGToYbgS/9Hj8IPKKq7YCfgSsrWlZLcMaYkHg5kkFEmgNZwFT3sQA9gVfcQ54FBlW0rJbgjDEhCaUG538jv7uNLHG6R4FbgaILwmlArqoWuI+zgWYVLatNl2SMCUkovQb+N/Ifdh6R/sAOVV0tIt0DnL7CPVKW4IwxofGuX7QLMEBE+gHVgbo4Nbr6IpLi1uKacwRj2K2JaowJSZJI0FsgqjpWVZuraiucmYY+UNVLgYXAUPewEcAbFS5rRV9ojKmcIjDY/jZgjIhswLkmN63CZbUbfaPLbvStXBLhRt9vcvYH/Z93dEbNqN7oa9fgoiyaA5KrJEevAl8lut9zcwRiYSLLYFmCM8aEJBZmCQmWJThjTEjiKL9ZgjPGhCaeJry0BGeMCUkc5TdLcMaY0MRRfrMEZ4wJURxlOEtwxpiQ2G0ixpiEZdfgjDEJK8kSnDEmccVPhrMEZ4wJSTw1URNiNpGlSxYzIKs3/fucy7SnSp1bz2JbbIvtkQjMJuIdVQ3bhrOYxHrgc+DP5R2fl68a6rbvQIH27NVL//fdZt3zy0Ht3/98Xf/l/0I+j8W22JUhthff6x9yD2qwWzjzSzBb2GpwItIeuBo4DegA9BeRdl7HWb9uLZmZLWmemUmVqlXp0y+LRQvf9zqMxbbYlTZ2SSIS9BZt4Wyi/g5Ypqr73amHPwQGex1kR04OjZs0Ln6cnpFBTk6O12EstsWutLFLiqcmajgT3Hqgq4ikiUhNoB+QWfIg/1V3KnJdQUuZIzNSvzkstsWuDLEPj+v5uqhhE7ZeVFX9UkQeBN4D9gGfAQWlHFe86k5FZvTNyGjM9m3bix/vyMkhPT29osW22BbbYpcjnkYyhLUXVVWnqerJqtoV2AX8z+sYx7c/gc2bN5GdvYX8Q4dYMH8e3Xr09DqMxbbYlTb2YeKojRrW++BEJF1Vd4hIC+AC4EyvY6SkpDB2/ARGjbyKwkIfgwYPoW1bz/syLLbFrrSxS4qBvBW0sC46IyJLcFbFyQfGqGrAbp/KuOiMMZHkxaIzu37xBf09Ta2VnLiLzqjq2eE8vzEm8mKh8yBYCTGSwRhjSmNjUY0xIYmnGpwlOGNMSOLpNhFLcMaYkFgNzhiTsCzBGWMSljVRjTEJK55qcHabiDEmJF6O1BKRPiLytYhsEJHbvS6rJThjTGg8ynAikgw8AfQFjgMuEZHjvCyqNVGNMSFJ8q6NehqwQVW/AxCRF4GBwBdeBYipBHck4+REZKQ79VLEWWyLXRliFwnleyoiI4GRfrum+JW/GbDF77ls4PQjL+GvEqmJOrL8Qyy2xbbYkaSqU1S1k9/mn5xLS5SeTriRSAnOGBNfsvntLN/NgR+8DGAJzhgTLSuBdiLSWkSqAhcDc70MEFPX4I5QNK9LWGyLXRlie0pVC0TkeuAdIBmYrqqfexkjrBNeGmNMNFkT1RiTsCzBGWMSVtwnOBGpLiIrROQzEflcRO6OcPxkEflERN6KZFw39iYRWScin4rIqgjHri8ir4jIVyLypYh4vqBQGXGPcd9v0bZHRP4cidhu/Jvcz9l6EXlBRKpHMPaNbtzPI/me41ncX4MTZ/XbWqq6T0SqAB8BN6rqsgjFHwN0Auqqav9IxPSLvQnopKo/RjKuG/tZYImqTnV7wGqqam6Ey5AMbAVOV9XvIxCvGc7n6zhVzROR2cB8VX0mArHbAy/i3P1/CFgAjFJVz5fiTCRxX4NTxz73YRV3i0jWFpHmQBYwNRLxYoWI1AW6AtMAVPVQpJObqxfwbSSSm58UoIaIpAA18fi+rQB+ByxT1f2qWgB8CAyOUOy4FfcJDoqbiZ8CO4D3VHV5hEI/CtwKFEYoXkkKvCsiq90hMZFyFLATeNptnk8VkVoRjF/kYuCFSAVT1a3Aw8BmYBuwW1XfjVD49UBXEUkTkZpAP357k6wpRUIkOFX1qWpHnDuhT3Or82ElIv2BHaq6OtyxAuiiqifjzMZwnYh0jVDcFOBk4F+qehLwC+D5VDeBuM3iAcDLEYzZAGcweGugKVBLRIZHIraqfgk8CLyH0zz9DCiIROx4lhAJrojbTFoE9IlAuC7AAPc62ItATxGZGYG4xVT1B/fvHcAcnOszkZANZPvVlF/BSXiR1BdYo6o5EYx5DrBRVXeqaj7wGtA5UsFVdZqqnqyqXYFdgF1/K0fcJzgRaSQi9d2fa+B8CL8Kd1xVHauqzVW1FU5T6QNVjchvcwARqSUidYp+Bs7DacaEnapuB7aIyDHurl54OMVNkC4hgs1T12bgDBGp6XZu9QK+jFRwEUl3/24BXEDk33/cSYShWk2AZ90etSRgtqpG/JaNKMgA5jjfM1KAWaq6IILxRwPPu03F74DLIxXYvQZ1LnBNpGICqOpyEXkFWIPTPPyEyA6delVE0oB84DpV/TmCseNS3N8mYowxZYn7JqoxxpTFEpwxJmFZgjPGJCxLcMaYhGUJzhiTsCzBGQBEZLCIqIgcW85xl4lI0yOI0z0aM6+YyskSnClyCc5MGReXc9xlOMOUjIl5luAMIlIbZ+jZlfglOBG51Z1v7jMReUBEhuJMDfW8OxdbDXdOuobu8Z1EZJH782ki8rE7GP9jv1EPxkRMIoxkMEduELBAVb8RkV0icjLOSIlBOHOt7ReRVFXd5S4ScrOqrgKQslc5/wro6i4scg5wPzAk/G/FmF9ZgjPgNE8fdX9+0X2cBDytqvsBVHVXiOeshzOErh3OtE5VPCqrMUGzBFfJuWMbewLtRURxlm9T4FWCmzi0gF8vdfhP330vsFBVB4tIK5xZXoyJKLsGZ4YCM1S1paq2UtVMYCPOdDxXuAPbEZFU9/i9QB2/128CTnF/9m+C1sOZThycjgljIs4SnLkEZy45f6/i9JTOBVa5syXf7D73DDC5qJMBuBv4h4gsAXx+53gI+D8RWYpTKzQm4mw2EWNMwrIanDEmYVmCM8YkLEtwxpiEZQnOGJOwLMEZYxKWJThjTMKyBGeMSVj/DzRSR1jugrnKAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from ml_utils.classification import confusion_matrix_visual\n",
    "\n",
    "confusion_matrix_visual(y_test, preds, np.sort(y.unique()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([<matplotlib.axes._subplots.AxesSubplot object at 0x134429B0>,\n",
       "       <matplotlib.axes._subplots.AxesSubplot object at 0x134AA150>,\n",
       "       <matplotlib.axes._subplots.AxesSubplot object at 0x134C31F0>,\n",
       "       <matplotlib.axes._subplots.AxesSubplot object at 0x134DF2B0>,\n",
       "       <matplotlib.axes._subplots.AxesSubplot object at 0x134FB370>,\n",
       "       <matplotlib.axes._subplots.AxesSubplot object at 0x13516430>,\n",
       "       <matplotlib.axes._subplots.AxesSubplot object at 0x135314F0>,\n",
       "       <matplotlib.axes._subplots.AxesSubplot object at 0x1354BBB0>,\n",
       "       <matplotlib.axes._subplots.AxesSubplot object at 0x13566690>],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 7 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from ml_utils.classification import plot_multi_class_pr_curve\n",
    "plot_multi_class_pr_curve(y_test, grid.predict_proba(X_test))"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
